Implementation of Web‐based argumentation in facilitating elementary school students to learn environmental issues

This research develops a Web‐based argumentation system named the Web‐based Interactive Argumentation System (WIAS). WIAS can provide teachers with the scaffolding for argumentation instruction. Students can propose their statements, collect supporting evidence and share and discuss with peers online. This research adopts a quasi‐experimental design, applying WIAS to the teaching of environmental issues, including mudslides, global warming and nuclear power. Fifty‐seven elementary school fifth graders from two classes participated in this research. With each class as a unit, they were divided into the WIAS group (n = 30) and the traditional argumentation instruction (TAI) group (n = 27). Before research, all students took the pre‐test of the ‘achievement test for environmental issues (ATEI)’ and the ‘environmental literacy scale (ELS).’ Then all students received argumentation training and six classes of argumentation instruction. Students in the WIAS group performed argumentation in the WIAS, while those in the TAI group performed argumentation in a traditional classroom. After the six‐class argumentation instruction, all students took the post‐test of the ATEI and ELS. The results show that students in the WIAS group have significantly better learning effectiveness than those in the TAI group. Students in the WIAS group also exhibited significantly better improvement in their environmental literacy.     

JITL-based concentration control for semi-batch pH-shift reactive crystallization of l-glutamic acid

Although concentration control (C-control) strategy has been shown to give effective and robust control performance for batch cooling and semi-batch antisolvent crystallizations in recent years, no research work was reported concerning the potential application of conventional C-control for the more challenging semi-batch pH-shift reactive crystallization that is common in the process industries. To this end, this paper presents detailed analysis to find out that it is not feasible to apply the C-control to semi-batch pH-shift reactive crystallization. To circumvent this problem, a variant of C-control strategy by incorporating the Just-in-Time Learning (JITL) method to cope with strong process nonlinearity inherent in the pH-shift reactive crystallization is developed in this paper. Simulation results are presented to illustrate the proposed design and a comparison with conventional optimal control is made.     

极限训练机在矿山开采最大下沉值估算中的应用

矿山开采过程中采空区地表往往会发生形变,研究其最大下沉值对安全生产具有重要意义,现有的开采最大下沉估算方法还有提升的空间。基于岩移数据决策与极限训练机(Extreme learning machine,ELM)算法,提出了一种估计最大下沉的新方法。该方法将采厚、倾角、平均采深、走向长度、倾向长度和覆岩岩性确定为最大下沉值影响因素;应用以Sigmoid方程为核函数、隐含层神经元个数为114的ELM模型对最大下沉值进行了估算。通过案例分析,ELM模型得到了优于传统算法如CHAID、Boosted Tree、ANN、BPNN和SVM的RMSE、MAE、MAPE、最大残差及秩相关系数,故认为该模型是一种有效的矿山开采最大下沉估算方法。     

Using the cloud to develop applications supporting geo-collaborative Situated Learning

Situated Learning stresses the importance of the context in which learning takes place. It has been therefore frequently associated with informal learning or learning outside the classroom. Cloud technologies can play an important role supporting this type of learning, since it requires ubiquitous computing support, connectivity and access to data across various scenarios: on the field, in the classroom, at home, etc. In this paper we first present the situated learning theory and how we can take advantage of services offered by Cloud Computing to implement computer applications implementing learning activities based on this theory, providing pertinent geographical information and discussion boards. Next we propose a software architecture schema which can be used as a basis for integrating existing cloud services into new applications supporting learning activities. Then we present two examples developed with this approach with its viability and advantages. These are discussed in the concluding chapter.     

基于奇偶树型交互学习机与遗传算法的密文优化系统

为使图像加密系统具备优化功能,并解决当前遗传算法无法实现全局最优、收敛速率慢等问题,提出奇偶树型交互学习机耦合全局离散遗传算法的密文优化系统。定义权值更新机制,耦合混沌映射,构造奇偶树型交互学习机及其互扰模型。将切断型轮盘赌择取机制引入均匀交叉算子中,以图像分块的相邻像素相关系数和密文信息熵为目标,根据权重理论设计加权适应度函数,提出一种全局离散遗传算法,最终形成"初始加密-密文优化"的加密结构。实验结果表明,与超混沌算法、离散遗传算法、元胞自动机相比,该系统的加密质量较好,并且具备全局优化功能,可优化所有迭代结果,使最终输出密文的信息熵最大,相关系数最小。     

JiTT—Blending Learning理念下的C++课程特色网站开发

Ji TT充分体现了Blending Learning的本质特点,把传统学习方式的优势和E-Learning(即网络化学习)的优势结合起来。Ji TT—Blending Learning理念下的C++课程特色网站突破了时间和空间的限制,将多种教学媒体、资源、情境混合,建立了课堂学习和网络学习相结合的模式,给教师和学生提供了一个方便、高效的通过网络教与学C++课程的平台。     

互动教学法对Java EE课程教学效果的影响研究

为了验证互动教学法对教学效果的影响,在《Java EE及其应用》课堂的教学环节中,采用对比的方式,选择一个班级进行传统教学,另一个班级除了传统教学外,还使用Java EE教学互动平台来进行课程教学,在课堂上和课后给学生提供全方位的互动教学。实验结果证明教学互动平台对于教学效果有明显的积极影响,尤其对于70~89分数段的学生来说具有较明显的促进作用,学生卷面成绩有明显提高。     

机会网络中基于元胞学习自动机的拥塞控制策略

对于机会网络中的多副本报文转发机制下由于节点缓存溢出而产生的拥塞现象,已有的控制策略仅考虑报文或者当前节点自身的信息,没有利用邻居节点之间的关系以及邻居节点上报文的存储情况来提高网络中报文的投递成功率。提出了一种基于元胞学习自动机的拥塞控制策略,主要是以局部环境中报文的特性来近似整个网络中报文的特性,根据报文所在节点的局部环境中周围邻居节点对该报文的持有情况,结合学习自动机的算法对报文的丢弃概率进行自动学习及更新。在报文复制时考虑对端节点的缓存熵信息,通过合理的报文的丢弃和复制,提高节点上缓存报文的信息量。实验仿真结果表明,该策略有效的降低了网络负载率和报文的投递延时,并提高了报文的投递率。     

非稀疏多核组合的支持向量回归方法

为了改善支持向量回归机的性能,本文提出了一种利用多核学习解决回归问题的算法(NS-MKR),算法对基本核函数的组合系数施加了Lp范数的约束(p>1),以得到组合系数的非稀疏解,并采用了两步优化方法,首先求解基于加权组合核的标准支持向量回归问题,用于学习拉格朗日乘子,然后采用简单的计算,求得基本核函数的组合系数,这两个步骤交替进行,直到满足事先定义的收敛准则。在人工数据集和真实数据集上的实验表明,相对于传统的单核和稀疏多核支持向量回归方法,本文提出的算法有更好的泛化性能。     

基于交互式多媒体技术的E-Learning系统研究

针对在线学习的多媒体技术,本文以在线教学为基础,以39个在校大学生为实验主体,研究了基于交互式多媒体技术的E-Learning系统。将实验主体随机分为使用LBA在线听课和在传统课堂听课2个对照组,并提供相同的授课时间和学习资料,通过衡量2组学习者考试成绩的等级和学习者的满意度来调查学习结果,实验结果表明,在线组的学习成绩高于课堂组的成绩,即使用LBA系统学习的学生比传统课堂下学习的学生能取得更好的成绩,而且在2种教学环境中学习的学生,其满意度相当。该系统可以帮助学习者更好地理解学习内容,取得比传统教学更好的成绩,为建立高效的基于多媒体技术的E-Learning教育环境开辟了一条重要路径。